a) Field of the Invention
The present invention relates to pattern recognition such as image recognition or voice recognition, and more particularly, is directed to a method of fuzzy-pattern-recognition by using a Fuzzy neuron.
b) Description of the Related Art
As an information processing technique imitating information processing within a living body, a technique using a so-called neuron is known. In this technique, a learning function which the living body possesses is imitated in terms of software or hardware, and hence information processing can be implemented in the form similar to the information processing which is carried out within the human brain.
A fuzzy theory is known as a theory handling fuzzy information. In this theory, a fuzziness which the information contains is represented by membership functions. Since evaluation of the fuzziness is performed, the fuzzy theory can also be considered to be information processing similar to that which is carried out in, for example, the human brain.
A recent investigation has been directed toward the fusion of the fuzzy theory and the neuron. More specifically, so as to be able to recognize a pattern represented by fuzzy information, in other words, a pattern such as a handwritten letter or a picture containing fuzziness, it is anticipated to combine the two. If such a combination can be properly realized, there can be implemented an information processing which is more human, that is, more similar to that which is carried out within, for example, the brain of a human being.
The technique derived from the combination of the fuzzy theory and the neuron still entail a number of problems which have not been solved. For example, consider the case where a membership function capable of describing the fuzziness of a known pattern is used in order to evaluate whether or not the input pattern which is an object of recognition matches the reference pattern. In this case, deciding what kind of membership function should be selected to represent the reference pattern presents a problem.
Consider the recognition of a two-color pattern separately colored "black" and "white", by way of example. In this case, the contents of the membership function to be prepared must be defined differently depending on whether the reference pattern is "a black pattern with a white background" or "a white pattern with a black background". If the reference pattern is "a black pattern with a white background", then a membership function which "regards white as a background" must be used. On the contrary, if the reference pattern is "a white pattern with a black background", then a membership function which "regards white as a pattern" must be used. The importance of presence or absence of defects must be also taken into consideration. In this manner, for the description of the reference pattern to be collated with the input pattern, the contents of the membership function used for the description must be appropriately selected.
Particularly in the case where a pattern consisting of a complicated "black" portion and "white" portion is intended to be recognized, either a large number of membership functions or a membership function having a multiplicity of peaks must be used. In addition, depending on the properties of the reference pattern to be described, it must be decided whether a membership function which "regards white as a background" or a membership function which "regards white as a pattern" is used as the membership function. Such setting would enable the recognition of a complicated pattern.